{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import numpy as np\n",
    "from scipy import signal\n",
    "from pylab import *\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False #用来正常显示负号\n",
    "fs = 256\n",
    "\n",
    "def rd(filename):\n",
    "    # filename = 'X:/3_2 运动算法资料/4_data/data.dat'\n",
    "    fid = open(filename, 'rb')\n",
    "    fid1 = fid.read()\n",
    "\n",
    "    data = []\n",
    "    ecg = []\n",
    "    acc = []\n",
    "    acc_x = []\n",
    "    acc_y = []\n",
    "    acc_z = []\n",
    "    #根据.dat写入格式，将两个8位组合成一个数据\n",
    "    for i in range(0, len(fid1), 2):\n",
    "        if fid1[i] >> 7 == 1:\n",
    "            data.append(((fid1[i] - pow(2, 8)) << 8) + fid1[i+1])\n",
    "        else:\n",
    "          data.append((fid1[i] << 8) + fid1[i+1])\n",
    "    fid.close()\n",
    "\n",
    "    #根据数据存储格式，将数组切片，前5位为ecg数据，后3位为三轴加速度数据\n",
    "    for i in range(0,len(data),8):\n",
    "        for j in range(5):\n",
    "            ecg.append(data[i+j])\n",
    "        acc_x.append(data[i+5])\n",
    "        acc_y.append(data[i+6])\n",
    "        acc_z.append(data[i+7])\n",
    "        acc.append(np.linalg.norm( [data[i+5], data[i+6], data[i+7]], ord=2))#加速度二范数\n",
    "\n",
    "    return ecg, acc, acc_x, acc_y, acc_z\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# butter带通滤波\n",
    "def butter_bandpass(data, fre, lowcut, highcut, order=3):\n",
    "    nyq = 0.5 * fre\n",
    "    low = lowcut / nyq\n",
    "    high = highcut / nyq\n",
    "    b, a = signal.butter(order, [low, high], btype='band')\n",
    "    new_sig = signal.filtfilt(b, a, data)\n",
    "    return b, a, new_sig\n",
    "# 对于丢数片段，进行插值处理，权重向量计算\n",
    "def interpolat(acc):\n",
    "    #以下求权重向量w_i\n",
    "    n = fs #需要拟合的点的数量\n",
    "    w_ii = [1/(n-ii) for ii in range(n)]\n",
    "    w_i = [x / sum(w_ii) for x in w_ii]\n",
    "    for num in range(len(acc)):\n",
    "        if acc[num] == 0:\n",
    "            if num > fs:\n",
    "                acc_i = acc[num-n: num]\n",
    "            else:\n",
    "                acc_i = acc[num+1: num+n+1]\n",
    "            acc[num] = sum(np.multiply(w_i, acc_i))\n",
    "    return acc\n",
    "\n",
    "# # 设置动态阈值\n",
    "def thr_mean(acc_norm): # 其中acc_norm长度为1分钟的倍数\n",
    "    acc_norm2 = acc_norm.reshape(t, 1, 768) #创建3维矩阵\n",
    "    acc_mean_t = [np.mean(acc_norm2[tt, :, :]) for tt in range(t)]\n",
    "    acc_mean = np.dot(np.transpose([acc_mean_t]), ones((1,768))).reshape(t*768)\n",
    "    plt.plot(acc_norm)\n",
    "    plt.plot(acc_mean)\n",
    "    plt.show()\n",
    "    return acc_mean\n",
    "\n",
    "# 通过积分方法PIM计算活动量\n",
    "# 具体算法1：以每分钟为计数单位，将每分钟分为6个10s片段，求6个片段中的积分最大值\n",
    "def pim(acc_norm, thr):\n",
    "    acc_norm = abs(acc_norm - thr)\n",
    "    acc_norm = acc_norm.reshape(t, 6, 128) #创建3维矩阵\n",
    "    # 计算每个epoch=10s的积分，并求最大\n",
    "    delte = 1/(fs/5)\n",
    "    acc_pim = []\n",
    "    for epoch in range(t):\n",
    "        acc_epoch = delte*sum(acc_norm[epoch, :, i] for i in range(128))\n",
    "        acc_pim.append(sum(acc_epoch))\n",
    "    \n",
    "    return acc_pim\n",
    "\n",
    "# 过零检测模式ZCM来计算活动计数\n",
    "def zcm(acc_norm, thr_zcm):\n",
    "    thr_zcm = thr_zcm*ones(len(acc_norm))\n",
    "    acc_zcm = acc_norm - thr_zcm\n",
    "    acc_zcm = acc_zcm.reshape(t,768) #768为1min的采样点\n",
    "    zcm_count = []\n",
    "    for tt in range(t):\n",
    "        c=0\n",
    "        for sam in range(767):\n",
    "            if acc_zcm[tt,sam]*acc_zcm[tt,sam+1]<0:\n",
    "                c+=1\n",
    "        zcm_count.append(c)\n",
    "    return zcm_count\n",
    "\n",
    "# 时间阈值模式TAT来计算活动计数\n",
    "def tat(acc_norm, thr_tat):\n",
    "    thr_tat = thr_tat*ones(len(acc_norm))\n",
    "    acc_tat = acc_norm - thr_tat\n",
    "    acc_tat = acc_tat.reshape(t,768) #768为1min的采样点\n",
    "    tat_count = []\n",
    "    for tt in range(t):\n",
    "        c=0\n",
    "        for sam in range(767):\n",
    "            if acc_tat[tt,sam]>0:\n",
    "                c+=1\n",
    "        tat_count.append(c)\n",
    "    return tat_count\n",
    "\n",
    "#   Webster's算法\n",
    "def webster(acc_act):\n",
    "    D = list(zeros(4)) #暂时将前4分钟及后2分钟赋值为0，后面会再次赋值\n",
    "    for i in range(4, t-2):\n",
    "        w_d = array([0.15, 0.15, 0.15, 0.08, 0.21, 0.12, 0.13])\n",
    "        a_i = array(acc_act[i-4: i+3])\n",
    "        d_i = 0.025*sum(w_d * a_i)\n",
    "        D.append(d_i)\n",
    "    D = D + list(zeros(2))\n",
    "    return D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 批量读取数据\n",
    "##当使用固定阈值1g时，th_sleep=28,th_deep=16 此阈值设置偏高，易把久坐不动判断为睡眠状态\n",
    "# th_sleep=18,th_deep=5  此阈值设置偏低，易把睡眠状态判断为清醒\n",
    "#当使用动态阈值acc_mean时,th_sleep=5,th_deep=1 仍将部分久坐判断为睡眠，那么考虑频谱分析\n",
    "path = \"F:/工作任务/睡眠监测/data/CA-30-F3-FF-FE-CD2-9/9403\"#文件夹目录 \n",
    "# path = \"F:/工作任务/睡眠监测/data/2018/2/10/FC-00-00-00-00-4F/43509\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/2018/2/4/FC-00-00-00-00-4F/43284\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/FC-00-00-00-00-4F/9442\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/FC-00-00-00-00-4F/9443\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/FC-00-00-00-00-4F/9445\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/FC-00-00-00-00-4F/9454\" \n",
    "# path = \"F:/工作任务/睡眠监测/data/14/FC-00-00-00-00-4F/9701\"\n",
    "# path = \"F:/工作任务/睡眠监测/data/2018/3/21/FC-00-00-00-00-4F/9859\"\n",
    "#path = \"F:/工作任务/睡眠监测/data/2018/3/21/FC-00-00-00-00-4F/9860\"\n",
    "# path = \"F:/工作任务/睡眠监测/data/2018/3/21/FC-00-00-00-00-4F/9862\"\n",
    "# path = \"X:/3_2 运动算法资料/4_data/Text2018-02-06/FC-00-00-00-00-4F/43285\"\n",
    "\n",
    "\n",
    "\n",
    "files = os.listdir(path) #得到文件夹下的所有文件名称\n",
    "acc_norm = []\n",
    "acc_x = []\n",
    "acc_y = []\n",
    "acc_z = []\n",
    "ecg = []\n",
    "for file in files:#遍历文件夹\n",
    "    file = os.path.join(path, file)\n",
    "    if os.path.isdir(file): #判断是否是文件夹，是文件夹才打开\n",
    "        filename = file + \"/\" + \"data.dat\" #data.dat文件目录\n",
    "        data = rd(filename)[1] #调用data_reading模块，返回加速度二范数（共5个返回值中的第2个）\n",
    "#         data_x = rd(filename)[2]\n",
    "#         data_y = rd(filename)[3]\n",
    "#         data_z = rd(filename)[4]\n",
    "        data_ecg = rd(filename)[0]\n",
    "        \n",
    "        acc_norm.extend(data)\n",
    "#         acc_x.extend(data_x)\n",
    "#         acc_y.extend(data_y)\n",
    "#         acc_z.extend(data_z)\n",
    "        ecg.extend(data_ecg)\n",
    "        \n",
    "\n",
    "# 读取单个文件数据\n",
    "# filename = \"F:/工作任务/睡眠监测/data/CA-30-F3-FF-FE-CD2-9/9403/18-02-09 05-33-20/data.dat\"\n",
    "# acc_norm = data_reading.rd(filename)[1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savetxt('X:/3_2 运动算法资料/4_data/Text2018-02-06/FC-00-00-00-00-4F/43285/43285.txt', acc_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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vPvztcfzulH7ZHgoA//jh0Hq7fnnCPvzg8PDE/vqT9q1/0rt6xF71dr3/66OZ\nddPxzdbdD4/o1STtFLfR+PlxfdPuM/7q4bz048NatOeiQ3tmYb0RNjuWZz+9OU6hNrz07GuAM4G6\nOY7DgGfcz+OBwVmkNUBERonIVBGZWlXlLcJgY3rv1NRNY4RH2OHa05W/03ZtadO6JKt8Mt3CimYM\nsyAF7vfVPVUVutykcETfiux3ilFVZxR7VV2jqqtTksqBuuj7K4CuWaQ1znu0qg5W1cEVFTlUNHDx\noS33JHO58MM8f37fqGGLb65onkuRx6jD5Rs/PaZP2CYYESaXAdp1QDv3cwc3D69pvrNjh+J5szQX\nUkUz6QKYenhxbeTyQZJ+ggtAkqswFwGeBtQ5KwcCc7NISwx+ikmhrq9ClBO2xib5ZvVMBOqgb9cO\n/Pm8A8I2IxBSr/Hhe+0EwL3n7J9+4wiRi9g/AtwoIncB+wKTs0grOLnc/F72ydfNUChSGyXr+flL\nIavzxAG7ZLX9Ib3DfRO0Y7vSWM1B98KDFwxu8L7H94f24LIjezHtumM4aYD3WWA7hRTnyrPYq+ow\n9/884FjgXeAYVa3xmua38V4wfSssSVtQu7kGUjXz+IqfHYLff6e/b3kVgiQOEpe3ad3gfY/BPTsj\nIvWu5MaXynf275Y2n7DukJze9VXVRWybaZNVWuHJZYA2eRdq3MilzbCGPTp4XBnUKCB2SnIkLh3Y\nVDNNC5PFoxcPSZsehfN8x/f2i4QdubLvLtsz/fpjs3ops/Gmze2aTjt67Njee0E5YmKfcArdKPlV\nXti99DiMb+y5k7+LuOTL3jtv89HvukO7FraMB53Lyxp89+uSSOfqPGVg8G9+m9gXETHQr3AIqF6C\nbmgLeT5T377OhzAbqKdHHew5JlYup65JByGb81OAk5l4sQ+qDv28j4M8zxpTR05OPvsCHp8SfuPZ\n3Kv6Qdh1+bDeGbfx8jRUXpbdm8x+Uucq+fN5gzj9gN3SbpNP3eVT7YW4lOITjDlHcqnEsG9iIx6E\ndZ18f2gPOpe3aXEKn99PFdkcat38+qi5wuo6A8f325kR3+qa+9KXATyxFaKqki/2MX1EN3IjWvLi\nIynX28j+u3BwgVdU8nIf1W1S1rp5h8EO7cua/a2QtDSl1nseLX9vjnRFFOKpNPFunNzcAR7yDWq2\nrN/nvMFLVT7nnQOp0UPjTASqsqB4EaNbTuvP2UO6c3if5uNc+RFyPFdyuf6zucu9CnYui6T4QeLF\nPg4kSTji+MQTtfcqelXkF8k1iOM5dM8udMsww6br9m255bT+lHpcaCZs0r1lHFaHqBDlxuOs5EEc\n4k0HqY8x1F4g19kQ/pWfOcSgfN8cAAAR00lEQVRxcLxw+aFZ7/PHlMV1/GburSey767b8+xlQ/PO\nq7l6S522WSievPTgjNtkdUk1mYzj3VU0bK/cov5mQ+LFPrDYODFR0dQ5vdFv9sIhnUvOy+kN6omg\nY5oVxxpEL02zz+k+useuOX7vgq84tn/3HQIvI6rX/9xbT2TAbsEff+IHaI1tFOIhp1AB4sZfPZzN\nNbUcc8d/t5Vd4FkSUX1ozNeuummWD73zdYP0TNWb37mPVmXmEuOpyRu0zRxSWPGjTOwjQLQu83jQ\nvQCvl+dL0oLCZTqefA63e+donE8vT2vNNWr5zMYpBIl34xQ7DUIcx6hZyTcQWjZCm65eolZThWo3\nOqVxIeVKNnW43+7BuzFyOaktVXvO91NIam9ib/hK2J3ZQrlWktZr9wMvNZLkamss/lHrMCRe7HOb\njePhUc7HqzbINw0bBEuI2tVn5ESm6yUOp7l/t45hm5A1mer1/nOjvTJX4sU+W352bN+EzcYJ24LC\nUsjjDXN6ZibCWEktmw7Qz47ry3OXDaWigKs2eXG75NMhGr73Tp62M599RBhYCN9hSMRpDdqoPIVE\nZZzDm4skWBnJlH3j3zOdw8E9O1PmvoAVlfNdCOwN2pgRlw5zg/nZMbqjcr0fgnqvws/9gibb87x7\n5/jHnjcyY2LvEpUbNyJm5IwNXMaLbju04/Wrjqz/HnSHIMyro9D3ePPz7AtrRx2JF/tcBmjjLriG\nD/hwEcSh3evQpjVtS7fFmC9UYx2XeyxddcThvKYj8WIfFHE54XGx028Kc9wFXCwlNexFcz3GAtni\nlaiMd4RFs7FxQjpTJvaGr4T9mF4MApOpIRPCqYeodyz8qpF83UHmxgkIrycmTIlIvjwVjlx6TWlv\nvogLVxhknI2TR6VF7R4IUpBt6mWE8DJIFdajWD5EZRC6EPh1rC25THIt4zv7d8t6nyi8nerHNd/c\nmEBUZor5GiY7GodUT9ZiLyKtRWS+iLzt/vUXkRtFZIqI3JeyXZO0uOBppaoIan26R/dmfb22XGPg\nNCeO39p1+2ALjpjIpCMMcY9KgxKn2DgDgKdUdZiqDgPKgMOAIcBSETlGRAY1TvPL4GzxenojcyEY\nOZPa2Fmjkxv53AdW594IyyuQS4jjg4GTRGQ48DEwC3heVVVExgAjgdVp0t5onJGIjAJGAXTv3j3H\nQ2iZwBYc9zOzANuZBrFx4tDlqye3GvbtGLNYYDtb8hVFT+E8wgiXUPASgyOObtpM5NKznwIco6pD\ngFKgHbDQ/W0F0BUoT5PWBFUdraqDVXVwRUXwy3IZhSB5N0kU8NRA+FD1Lc2zj3vP3Uvj3FJnoe6p\nJ996CKsec+nZf6Sqm9zPU9km+AAdcBqQdWnSQsHrY2mc+ry5Yp6q9KStFw9xYDLVZ3M3ddC9xqBO\nc8aVqhodcNKvt0zHF7X3IXIR4cdEZKCIlACn4vTiD3N/GwjMBaalSYs0WYcCjmA3J+1aqtEzMzCC\nEJeWsszVv11M56Q5Et4OtEicliX8HfAkzvn6N3ATMEFE7gKOd//mAbc0SosFYV+Efg8UZ1qo2m/8\nu45DPhMFGkfxvk/mveLsZy7EU0A2ZfhzHYetJg3JWuxVdSbOjJx63Nk2JwJ3qerXzaWFgefZOIFa\nEQ3iNeMo+sIVVm1mKjes85yE5cZjdYtkiS8LjqvqRuC5TGmhEIfZOAESRZdBK4HaCNrlN3E8xJYa\nCj8WHA/zevS0mHgB7IuTzz7xFHqlqnhNiWwZL9VSiJ5nNqen0LUf+EtVEabO1dT4Eoha3Do/LlEL\ncVxgvApp6olJkvimEpWjahWgIbncpIW+94bs0Tn7nSLQa/YyauCVqN9jUXwizpfEi31Qg1ZRHAxL\nG3u78GZkJCqP015oqfGIqn83H7uKffGZJK0/3ZjEi33UexBJw9ONUOSnJK5ikS3ZNDqFuCT8bpzj\ndhknXuy9ugzCbBSC7CE2EJaIXJ1BunH8wsv1kGkbP0Xdy3sgqdsE0aBku+C4ES0SL/ZB4euNHOhN\nUuB59h4cR7ksFemFnBcOz2mv6CpbWG1pPmGYo+YSi+7ZzR0T+3RE7MJLGla92+jYrjRsExpQqDn6\nYYi7t9g4hS2vkJjY1xG1MxMAcXqpKok9qzpSe7ffHbRbeIZkTXbz7ON0vXkh28OJ2uGb2OeIn2JU\nMJ99AfBSXpxEoOUoiJn2brky2pa2opXHAQxvLyxFq4nMzp74XBNxJfFin4t/OMw4aMVwyQel9XFZ\nvCToeEXhhUvIp9KDP2HZ1EvUGk4/SLzYx6gTGThRqYqgBmihcMfohxYIkqPBwR1lPvHsox4uwQtR\nXRjGDxIv9rlQ6AXHgxSo1JurEA1f2DdzLsUXukOQSx15i3oZLVq6j5r+EpWuiHcyxrOP2DGZ2Dci\naU8CDV0G0Ti4qIWzDWJ/yCzqIsGck2ic5ewoyDXhYZuwOytBYmIfMRJ8rdVTiHn22dy0acNMeBDq\nXMl6oZwC0mLUywz7Jlkok0DixT6wePYRvLBzES3fbfBQMUHqm995BxEbp84vHqj7LsC8k0zUGl8/\nSb7Yx+zsBSqEEamKOJwTL+vL5nsc2c0OSd0v8zZBkHGAtlETk87MMAc3Y3DZBUrixT6nAbuA8jUc\nvEUWLFwNJ00Ewjoem1bfkKhdV4kXe69ke2LiMg+30FZ6eqkqeDMiMT2uubqIYGw6oxF+XD1RO7eJ\nF/tcKjxqLXI+RLFNCu6lqnhQd04E73XhKciYDxLlZycmaveRt5lP8VlrIVsSL/ZxwK+bItPNHhVf\neSGmgEZlmmmLBGZiMBkH+bQUg7OVhnhZnXix96pv2YpDXFr3KLgzGhOHePaZUPy51aPWKLW84HjL\n+xbiDdqHvj+4Sdqzlw31tG/e717kt3voJF/sczhFSV3IJCoXq7c3lPMjm0Yu1/OdccZOczbkeXBR\nOY/5kGudH71PV7rt0K7++9irjuDAnp25/bsD+d5gHyOI+hEOIyJP0nUkXuyDInr95WaIjaH5I9k4\nwUOkrhHIymfvqducu01+0HTqpfdz0ZIwnrZ/twbfj923KwC/OWlf+nbdDoAzBu3GbWcM9Fxe83bk\nnUVkMbF3CfMkR+1RPh+8hTgO3o58KYSJIsGc+TjUbzbcceZ+YZuQCAIXexF5SEQmish1QZeVvvww\nSo0mUamLIKNeGoaRnkDFXkROA0pUdSjQS0T6BFleOkpLvB1i+7ISwBGiEg8jiF7z9UJZayevtq1b\nUZKHEKbrJ6ba2a60ZFuZPtqfSqsM2ZaWtKK8TeuM+bRPsdU7QlmJ1JfjlXTb1p2T1s1cC0LmRqsu\nj6b7Ovu1Ky3xbGdqWc2VW+cKaVvaijbNlN2YNqUNt2vXQr1nOt7G11+6qmvj5l93j7V1y8/muq8/\nNyX+dxrqjrHOztR6lEa/ZdKJ0gDsy4fMd11+DAOecT+PBQ4Dvqz7UURGAaMAunfvnnMhr/30cF6b\nWcmsyrUsW7eJ3Tu355/TF9K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      "text/plain": [
       "<matplotlib.figure.Figure at 0x171fc9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZuZ8KDBKRPjg1iQ3u8zuAvBDKAsUxVkSKRKSouLg4hK9njDEmFHVO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\nAdPYcmWwwU4CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x167dc978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Emw7aSuAp4FpjzBJgFrtDMgOB2izSFEVRlAAod7HPZcARwHUich3wN+BCEekBjAaOwnpy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\nJYCKvaIoSgnw/68WEUe+Q3NDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d44be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "12.8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.title(\"acc_norm采样频率51.2\")\n",
    "plt.plot(acc_norm)\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"ecg采样频率256\")\n",
    "plt.plot(ecg)\n",
    "plt.show()\n",
    "\n",
    "acc_down = array(acc_norm).reshape(int(len(acc_norm)/4),4)[:,0]\n",
    "\n",
    "plt.title(\"采样频率51.2/4=12.8\")\n",
    "plt.plot(acc_down)\n",
    "plt.show()\n",
    "\n",
    "acc_norm = acc_down\n",
    "\n",
    "51.2/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "04:55:20\n",
      "295\n",
      "17720\n"
     ]
    }
   ],
   "source": [
    "# 读取起始文件名，获得监测时间\n",
    "\n",
    "def t2s(t):\n",
    "    h,m,s = t.strip().split(\":\")\n",
    "    return int(h) * 3600 + int(m) * 60 + int(s)\n",
    "def t2m(t):\n",
    "    h,m,s = t.strip().split(\":\")\n",
    "    return int(h) * 60 + int(m) \n",
    "def s2t(seconds):\n",
    "    m, s = divmod(seconds, 60)\n",
    "    h, m = divmod(m, 60)\n",
    "    return \"%02d:%02d:%02d\" % (h, m, s)\n",
    "def m2t(minutes):\n",
    "    h, m = divmod(minutes, 60)\n",
    "    s = 0\n",
    "    if h >=24:\n",
    "        h-=24\n",
    "    return \"%02d:%02d\" % (h, m)\n",
    "\n",
    "t_start = files[0][9:].replace('-',':')\n",
    "m_start = t2m(t_start)\n",
    "s_start = t2s(t_start)\n",
    "print(t_start)\n",
    "print(m_start)\n",
    "print(s_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r/3km704Z3+rl2D3G/VYCGuxjonumNcMg+JG2oYfPpaf2484vnJR2HqcDTTZD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c680a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "768.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axis_s = range(m_start*768,m_start*768+len(acc_norm))\n",
    "\n",
    "plt.xticks([tt for tt in range(m_start*768,m_start*768+len(acc_norm),768*60)],\n",
    "  [m2t(tt/768) for tt in range(m_start*768,m_start*768+len(acc_norm),768*60)])\n",
    "\n",
    "plt.title(\"raw acc_norm\")\n",
    "plt.plot(axis_s, acc_norm)\n",
    "plt.show()\n",
    "\n",
    "3072/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VLVbV4vz8/ChnhYgodUU72B8G4OgjlRuD4xERkYVoB9+lqGu66Qug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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c6805f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对于丢数的片段（最长不超过5s），通过插值法补数\n",
    "\n",
    "acc_norm = interpolat(acc_norm)\n",
    "\n",
    "plt.title(\"acc_norm after interpolation\")\n",
    "plt.xticks([tt for tt in range(m_start*768,m_start*768+len(acc_norm),768*60)],\n",
    "  [m2t(tt/768) for tt in range(m_start*768,m_start*768+len(acc_norm),768*60)])\n",
    "\n",
    "\n",
    "plt.plot(axis_s, acc_norm)\n",
    "plt.show()\n",
    "\n",
    "# plt.title(\"acc_x\")\n",
    "# plt.plot(interpolat(acc_x))\n",
    "# plt.show()\n",
    "# plt.title(\"acc_y\")\n",
    "# plt.plot(interpolat(acc_y))\n",
    "# plt.show()\n",
    "# plt.title(\"acc_z\")\n",
    "# plt.plot(interpolat(acc_z))\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TelEgcNTebalwKJL3/jKQ4x6ZCECzRlb+H105iOLCArq0KuP739Zxpr3q975T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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148a9c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n\\nprint(\\'b:\\',b)\\nprint(\\'a:\\',a)\\n\\ndef butter_highpass(data, lowcut, fre, order=3):\\n    nyq = 0.5 * fre\\n    low = lowcut / nyq\\n    b, a = signal.butter(order, low, btype=\"high\")\\n    new_sig = signal.filtfilt(b, a, data)\\n    return new_sig\\nacc_norm_butter = butter_highpass(data, lowcut, fre, order=3)\\n\\nplt.plot(axis_s, acc_norm)\\nplt.plot(axis_s, acc_norm_butter)\\nplt.show()\\n'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 进行butter带通滤波\n",
    "data = acc_norm\n",
    "# fre = 51.2\n",
    "fre = 12.8\n",
    "lowcut = 0.5\n",
    "# highcut = 11\n",
    "highcut = 6\n",
    "\n",
    "b, a, acc_norm_butter = butter_bandpass(data, fre, lowcut, highcut)\n",
    "\n",
    "plt.title(\"acc_butter through butter filter\")\n",
    "plt.xticks([tt for tt in range(m_start*768,m_start*768+len(acc_norm),768*60)],\n",
    "  [m2t(tt/768) for tt in range(m_start*768,m_start*768+len(acc_norm),768*60)])\n",
    "\n",
    "plt.plot(axis_s, acc_norm)\n",
    "plt.plot(axis_s, acc_norm_butter)\n",
    "plt.show()\n",
    "\n",
    "acc_norm = acc_norm_butter\n",
    "'''\n",
    "\n",
    "print('b:',b)\n",
    "print('a:',a)\n",
    "\n",
    "def butter_highpass(data, lowcut, fre, order=3):\n",
    "    nyq = 0.5 * fre\n",
    "    low = lowcut / nyq\n",
    "    b, a = signal.butter(order, low, btype=\"high\")\n",
    "    new_sig = signal.filtfilt(b, a, data)\n",
    "    return new_sig\n",
    "acc_norm_butter = butter_highpass(data, lowcut, fre, order=3)\n",
    "\n",
    "plt.plot(axis_s, acc_norm)\n",
    "plt.plot(axis_s, acc_norm_butter)\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.33664766,  -4.33240367,   6.04856251,  -1.3266253 ,\n",
       "         3.18979169,  12.24409732,  -4.9894325 ,  -7.03478363,\n",
       "        -3.50941745,   2.4169952 ,   3.36501336,  -1.00376852,\n",
       "         4.62802971,  -5.64340017,  -7.82025747,   2.1763342 ,\n",
       "        -1.72125032,   2.39531676,  -3.30205955,   2.55013161,\n",
       "         8.53959431,   2.45530298,   2.50574208,  -1.34932567,\n",
       "        -2.12475761,  -3.72590717,   1.1185569 ,  -5.97963932,\n",
       "         0.18610186,   2.93198244,  -7.49227984,   0.50073203,\n",
       "         5.36539935,  -1.82000442,   1.44927256,   5.70723847,\n",
       "        -1.96213409,  -1.40800051,   1.06961215,  12.25341479,\n",
       "        -1.10856268, -11.92478509,  -0.08297421,  -2.74543215,\n",
       "        -1.51866113,  -2.14214716,   4.37651761,  -0.49208227,\n",
       "         0.59808162,  -0.43736363,  -2.81142989,   3.16212807,\n",
       "        -2.75693553,   4.04292073,   0.31038549,   7.40536523,\n",
       "        -1.3547595 ,  -3.71748001,   1.32398363,  -2.98682368,\n",
       "         1.26978791,  -4.58152772,  -0.88670504,   2.17775792,\n",
       "         0.93069872,   0.45822443,   0.41564092,  -3.54319387,\n",
       "         5.18954753,  -4.76878997,  -0.44284924,  -0.07852406,\n",
       "         4.7750347 ,   6.49010214,   2.28677786,   5.38127182,\n",
       "       -19.19544829,   1.74474612,   0.64788256,   1.35713362,\n",
       "        -2.10075784,  -2.5706292 ,   6.15068791, -10.83310436,\n",
       "        -1.39269778,   4.78834703,   3.94699156,  10.34389181,\n",
       "        -3.13258845, -10.61435341,   8.5830172 ,  -2.63218242,\n",
       "        -2.68275188,  -3.25054591,   1.79993534,   0.16852025,\n",
       "         6.58187114,   4.20604325,  -6.10715859,   2.26787886])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_norm[17000:17100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "394.890625"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_epoch = int(len(acc_norm)/(0.5*fs)) #10 s片段数量\n",
    "t = int(num_epoch/6) #1 minute片段数量\n",
    "acc_norm = array(acc_norm[:int(3*fs*t)])\n",
    "len(acc_norm)/128\n",
    "int(len(acc_norm)/(0.5*fs))\n",
    "50546/128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 活动计数\n",
    "\n",
    "# 通过积分方法PIM计算活动量\n",
    "# 具体算法1：以每分钟为计数单位，将每分钟分为6个10s片段，求6个片段中的积分最大值\n",
    "num_epoch = int(len(acc_norm)/(0.5*fs)) #10 s片段数量\n",
    "t = int(num_epoch/6) #1 minute片段数量\n",
    "acc_norm = array(acc_norm[:int(3*fs*t)])\n",
    "\n",
    "\n",
    "# 通过积分方法PIM计算活动量\n",
    "thr_h = 0 #设置固定阈值为0\n",
    "# thr = thr_mean(acc_norm)\n",
    "thr = thr_h*ones(len(acc_norm))#带通滤波后，选择0为阈值\n",
    "acc_pim = pim(acc_norm, thr)\n",
    "\n",
    "# 通过过零点ZCM计算活动量\n",
    "thr_zcm =30*ones(len(acc_norm))\n",
    "# thr_zcm =135*ones(len(acc_norm))\n",
    "acc_zcm = zcm(acc_norm, thr_zcm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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7xx8XN71Ie7vlFndwHzDA/cgj3Q86KLzv3dt99OgwzocfupeVue+yi/uoUe6H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\nABGRLkoBICLSRSkARES6KAWAiEgX9f8BtsCTw/s56+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d5b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JRlXFGNMaUcyhwPEi8mcRuRJ4N3CtMcYAtwJHRq0kInNFZKGILFyx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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128a7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#更新横坐标\n",
    "axis_m = range(m_start,t+m_start)\n",
    "# axis_t = [m2t(a) for a in axis_m]\n",
    "\n",
    "# ax1=plt.subplot(111)\n",
    "# xmajorLocator = MultipleLocator(60)\n",
    "# ax.xaxis.set_major_locator(xmajorLocator) \n",
    "\n",
    "# 做映射，设置x刻度：用axis_t来显示刻度\n",
    "plt.title(\"score of each minute by PIM(red)/ZCM(blue) \")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "\n",
    "plt.plot(axis_m, acc_pim, 'r')\n",
    "plt.plot(axis_m, acc_zcm, 'b')\n",
    "# plt.savefig(\"活动量-PIM\")\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"score of each minute by ZCM(blue) \")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "\n",
    "plt.plot(axis_m, acc_zcm, 'b')\n",
    "# plt.savefig(\"活动量-PIM\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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msS1GaWGEK1bOpaigf6ll057gitNTMzDtXapOml3OyvlTuKe6hg+9aWFGJvTI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3a17b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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djWVocfVzOISuRh99WhKZ5hbgHSLyR8AJ3FXZJilVXQpl2JmRIkcHrRJIf2Ai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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b97da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#   Webster's算法 D = 0.025(0.15A_4 + 0.15A_3 + 0.15A_2 + 0.08A_l + 0.21Ao + 0.12A+l + 0.13A+2)\n",
    "# acc_act = acc_pim #积分法计数获得的按分钟活动量\n",
    "# acc_act = acc_zcm #过零点法计数获得的按分钟活动量\n",
    "                     \n",
    "D_pim = webster(acc_pim)\n",
    "D_zcm = webster(acc_zcm)\n",
    "\n",
    "plt.title(\"Webster's score by PIM\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, D_pim)\n",
    "plt._show()\n",
    "\n",
    "plt.title(\"Webster's score by ZCM\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, D_zcm)\n",
    "plt._show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QKfUOk8ckRN1LZ3Mk0rmazBKZqXBG4dmJKMeGQvT5XbR7ZGJWOcaOfc3fEqnk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\n+P/+Q+MIRLI//wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11211828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qNbLpEiDL7IvsZx9JYG0kpjVoRSZUm2Cf9tS4uzX7sGOTAoNqr9URBlVBu11F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\nCvYiIjXw/wGD3p7r0vPePQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x157cf860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "th_sleep = 1.2 #将sleep/wake阈值设置为20\n",
    "th_deep = 0 #将deep/light-sleep阈值设置为10\n",
    "# dic_D = {t: D_pim[t] for t in range(t)}\n",
    "dic_D = {t: D_zcm[t] for t in range(t)}\n",
    "\n",
    "\n",
    "dic_score = dic_D.copy() #字典浅复制\n",
    "for i in range(4): #Webster's算法未计算的前4分钟状态同第5分钟\n",
    "    dic_score[i] = dic_score[4]\n",
    "for i in range(t-2,t): # 最后2分钟状态同前一分钟\n",
    "    dic_score[i] = dic_score[t-3]\n",
    "\n",
    "\n",
    "for key in dic_score.keys():\n",
    "    if  dic_score[key] > th_sleep:\n",
    "        dic_score[key] = 2\n",
    "    elif dic_score[key] > th_deep:\n",
    "        dic_score[key] = 1\n",
    "    else:\n",
    "        dic_score[key] = 0\n",
    "    # print(dic_score[key])\n",
    "    \n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_D.values())\n",
    "plt.show()\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_score.values())\n",
    "plt.show()\n",
    "# D_zcm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z9y44Pu1aPWazqjEJ0CQ4Lt/vRvkaNFC/nH1+KOm4lb2mMWntdm4G7ZTTSmaz\nqjHBPl8lcrFTMqhOJWdfblJVxOQWVVnO2U9/wXEvS2i2fo0J9qtz9t2hE6qApbRBpQuOl5xUBUws\nb1+fnH2y6ExEOGdvtg6NCva9vfPF0kG1+kJopd6EJjz+f2lB9hrk7AG6UY/6+mazqrHBvmzOvupJ\nVd0RcvYwuU8cWcZr2j3p3jRaHWr1mM2qxgT7/Lj0sjn7duU5++5oF44nVAwt69lPO7j2frKqQ60e\ns1nVmGBftOD4bOfs0yA44Zz9tINr77KQdaivbzarGvPKyVeJHD1nX82kqlEWHE/2n0y7lkbjTDlH\nno3zz3r20x73bzarGhPs51qtlVUvS+fsq03jlL+WMNl21aZn75y92Vg0JtivKnHcKblISLvqEsej\nfeKY1AImS0MvnbM32xAaE+yTGacryyWMkrOvomff7QYRlJxUlQ1JnPCkqinnyHtHHdWhvr7ZrGpM\nsG+tWrykXOCocvGS5QA7fN9Jt6tTs5z9Yqce9fXNZlVjgn1+6GUnyuWjszeESZUl6JX10sv0pic9\n/r9uOfsn08JG7tmbrU1jgn2y4Dg9i1fXt2fvnP2y7I3vicVkLQIXQjNbm8YE+6XRK2nvebFTLmcv\niZaqydlnE6TqUI1zqVzCtHv26c9/YtE9e7P1aEywb+WC4ygVFOdyE7ImZZQAm3/zGre65eyfTIO9\nc/Zma1Mq2Eu6SNJNks7t8/h+kq6QdJWkv5O0p6Q5SfdIuj79Oma8TR9Nflx62XH2kKWAKujZx1p6\n9pOZVFWbnH1rZbB3z95sbYYGe0lnAO2IOBHYJunIgt1eB7wvIk4DHiRZb/ZY4NKIWEi/7hhnw0eV\n5X4X19Sz18Ry471GKSs815ScfXtlGmfa9fXNZlWZV84Cy0sSXgWclN8hIj4UEVendzcD3wNOAE6X\ndHP6yWDVEoiSzpa0Q9KOXbt2rekXKCvfsy+7SAhkqyVNvlzCYq1y9vWoZ7+cs++suG9moykT7TYB\n96W3Hwa29NtR0onA/hHxBeAW4GUR8UJgD+BV+f0jYntEzEfE/ObNm0du/CiynH2WF09mY5Y7tq3J\nLgGYGSV1UlUhtGnnyNu5nP20ryGYzaoyC44/CuyV3t6HPm8Qkg4ALgB+Md305Yh4Ir29AyhK/1Rm\nebx8cj9Z67Vkz77GOftJT6qadpXJdi5nP+1rCGazqswreSfLqZvjgLvyO0jaE/gocE5E3J1uvkTS\ncZLawGuB29ff3LXLV4kcpc5K9Tn7ESZVTThnP+1x7XO5nP20ryGYzaoywf5y4PWS3gf8MvAVSe/K\n7fMm4PnA29KRN2cC7wQuAb6feKQNAAAHuUlEQVQE3BQR14yx3SNblbMfoc5KkrOvYOjlGnL2k+rZ\n1yVnv2pSlXv2ZmsyNI0TEbslLQCnAu+NiAfJ9dIj4kLgwoLDjx1HI8chHxw7JSdVQfU5+1Fm0E4q\nvdSp2aQqD700W58yOXsi4hGWR+TMpKXg2DPOvnTPvqWJXQjtVcec/bQviHpSldl4NGbQcn6B7tFy\n9q2J5cZ7jdKbXk5LTXilKufszTaExgT7dq5KZNlFQpJjq0njZDn7WhVCm3rOPjepymvQmq1JY145\nq3r2o+TsW9VMqlrLOPvJL15Sj5y9C6GZrU9jgv2qQmgj5+wn1rQlo+Ts829e49Z1zt5sQ2lMsF89\n9LJ8uYS5inr2o/SmqyqXMPWe/VLOvrPivpmNpjHBvmhS1Ug5+you0C7l7MtPqppkzr7dEpp2uYRV\nOXsHe7O1aEyw7+3ZRwSdkguOQ5azr3IN2uHtyt4PJpmzr0Ngzd7UPM7ebH0aE+x7c/ZZ3K7bOPvu\nSDn7lSWbJ9GWaefrYXUhNOfszdamMcG+t2c/6pJ7+cXKJ6VWOftO+TTXJLWdszcbi8YE+94Zp6OO\nIW+3WhUVQiufqpj84iXdqU+oAg+9NBuXxgT7uZ5JVaOONGm3qllwfJRCaMtpqcnNoK1DYF1d4rgx\n/7JmY9WYV062UEmnG8tjyEcpl1CznD2k6aUJtasbUYv8+FLOvuPFS8zWo0HBfnXPfqQLtBXm7OtQ\nxqEuOftWS0jwxFPZGrTTb5PZLGpMsJ8ryNmPMqlqsWblEiDt2U9ynH1NAutcS16D1mydGhPs2z05\n7uWcfbljWxMMqr0WR5hUBUm7Jrl4ybSXJMy0W/KyhGbrVI9XcwWWg31P3ZdRyiVUmLMvG2PnJrg2\nbieCusTVtuScvdk6lQorki6SdJOkc0fZp8xxVSnq2dc3Z192IfTW5BYv6dSrZ/9UNlKpJqkls1kz\ndKUqSWcA7Yg4UdJfSjoyIr45bB/gmGHHVSkL7B/4zLf4i899F6B0uYS5lnj4sSc59X2fnVj7AB5+\n7ElgtJ79399+P7d89+Gxt+X+7z/O1gM3jf1512KuJ9/mnr3Z2pRZlnCB5SUJrwJOAvJBu2if5w07\nTtLZwNkAW7duHanho9pvrz34zZOP4L7vPw7A8Vv354QjDih17GueeygPPfokweR790cctIkfm2uX\n2ve3XrKNW+4af6AHOHLLPrz02U+fyHOP6ncWfopb73mErQdsYu89y50bM1upTLDfBNyX3n4YeH7J\nfYYeFxHbge0A8/PzE42kknjbq49a07HHP3N/jn/m/mNu0fq98d8cwRv/zRHTbsbEnXXytmk3wWzm\nlUkYPArsld7ep88xRfuUOc7MzCpQJgDvJEnBABwH3FVynzLHmZlZBcqkcS4HbpT0DOCVwK9IeldE\nnDtgnxOAKNhmZmZTMLRnHxG7SS7AfgF4aUTcngv0Rfv8oGjbeJtuZmZllenZExGPsDyypvQ+ZY4z\nM7PJ80VTM7MGcLA3M2sAB3szswZQVFDgqwxJu4C71/EUBwEPjak5tpLP7WT4vE5Ok87tMyNi87Cd\nahPs10vSjoiYn3Y7NiKf28nweZ0cn9vVnMYxM2sAB3szswbYSMF++7QbsIH53E6Gz+vk+NzmbJic\nvZmZ9beRevZmZtaHg72ZWQPUNtj3WdN2i6TbBhwzJ+keSdenX8ek27/Us+3UKtpfZ73ntt8563Pc\nqvMo6cqeba+v7reopz7/tx+S9PMDjvH/bQm5/9s395ybL0n6sz7H+NymShVCq9qAdW//B8sLohQ5\nFrg0It7a81wHAl+PiF+ZbKtnQ/7cAseTO2d9jlt1HiWJ5LrPwiTbPCv6rMV8MHBwRHxywKH+vx2i\n4P/2/0XEheljFwAX9znU5zZV1579Arn1ayWdAjwGPJjtJGkvSR/pOe4E4HRJN6e9gDngRcALJX1e\n0uWS9q3mV6itBVae23lWnzMAJH2s57ii8/gzwHGSPifpM5IOqeh3qKsFVp7bBeDPgbsk/UK2k/9v\n12SB1WtaI+lQYEtE7Ejv+9z2Uddgn1+/9ieB/wL8Ye9OEfF4RLyuZ9MtwMsi4oXAHsCrgO8AL4+I\nFwNfBt444bbXXf7cHsDqcwZARJzRc1zRedwNvDIiTgI+AvzB5Jtfa/lz+3Tgq8B7SYLLW8D/t2uU\nP7db0tu/C1yY7eRz219dg31+/VqAD0XE94cc9+WIeCC9vQM4kuQP+63ctiZbtTZwwTkrUnQeHwDu\nKHFsU+TP7X8DtkfEg8BfAS/tc5z/b4db9X8rqUVyTq8fcJzPbaquwT6/fu2vAb8r6XrguZL+os9x\nl0g6TlIbeC1wO3A+kF0c+6V0W5Plz+3bCs5ZkaLz+NvA7+S2NVn+3J4LbEvvz9O/0J//b4crWtP6\nZOCLMXiykM9tJiJq9wU8jeQP8D7ga8B+PY9d33N7L+AjPfd/luRj2R3A+em2Q4AvAneS5E/3mPbv\nV7Nze1z+nPXs+7Ge26vOI8lH62vSYy/r/Ts18avo/xb4KHADcBNwaLqf/2/Hc27fDZyR28/nts9X\nbWfQStofOBW4IZKPwTYmPreT43M7OT6361PbYG9mZuNT15y9mZmNkYO9mVkDONibmTWAg72ZWQM4\n2JuZNcD/B4A2/YVF7L1DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a39bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4uHP1GDOpVMYATcRx4XEz8ueggfJ59tmppMMm7zWNUVOvZ1bQjtlWMmZSqYzY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\neMINAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfb70ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From 04:55 enter sleep monitoring program...\n"
     ]
    },
    {
     "data": {
      "image/png": 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THoxr6/+Wh3H/QhSVOMZ+KXBP+PkhYHmbOjcCR7r7wcAi4Ng467n7enefdPfJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d8bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  因刚开始入睡时，在PSG检测到进入睡眠状态1时，人体已经停止动作静止；故设置此重新评分法则：\n",
    "# (a) After at least 4 minutes scored as wake, the next 1 minute scored as sleep is rescored wake;\n",
    "# (b) after at least 10 minutes scored as wake, the next 3 minutes scored as sleep are rescored wake;\n",
    "# (c) after at least 15 minutes scored as wake, the next 4 minutes scored as sleep are rescored wake;\n",
    "# (d) 6 minutes or less scored as sleep surrounded by at least 10 minutes (before and after) scored as wake are rescored wake;\n",
    "# (e) 10 minutes or less scored as sleep surrounded by at least 20 minutes (before and after) scored as wake are rescored wake.       \n",
    "dic_rescore = dic_score.copy()\n",
    "for key in dic_rescore.keys(): # rule (a)\n",
    "    if (key > 3 and all([dic_rescore[key-i] == 2 for i in range(1,5)])and dic_rescore[key] < 2 ):\n",
    "        dic_rescore[key] = 2\n",
    "        break\n",
    "\n",
    "#         i = 1\n",
    "#         while(i<5):\n",
    "#             if dic_rescore[key-i] == 2:\n",
    "#                 i+=1\n",
    "#             else:\n",
    "#                 break\n",
    "#         if(i == 4):\n",
    "#              dic_rescore[key] = 2\n",
    "# plt.plot(array(range(t-6))/60, dic_rescore.values())\n",
    "# plt.show()    \n",
    "for key in dic_rescore.keys():# rule (b)\n",
    "    if (key > 9 and key < len(dic_rescore)-3 and all([dic_rescore[key - i] == 2 for i in range(1, 11)])and dic_rescore[key + 1] < 2):\n",
    "        for i in range(1, 4):\n",
    "            dic_score[key+i] = 2\n",
    "        break\n",
    "                \n",
    "for key in dic_rescore.keys():# rule (c)\n",
    "    if (key > 14 and key < len(dic_rescore)-4 and all([dic_rescore[key - i] == 2 for i in range(1, 16)])and dic_rescore[key + 1] < 2):\n",
    "        for i in range(1, 5):\n",
    "            dic_score[key+i] = 2\n",
    "        break\n",
    "                \n",
    "for key in dic_rescore.keys():# rule (d)\n",
    "    if (key > 9 and key < len(dic_rescore)-17 and all([dic_rescore[key-i] == 2 for i in range(1,11)])and dic_rescore[key + 1] < 2):\n",
    "        i=2\n",
    "        while(i<=6):\n",
    "            if dic_rescore[key+i] < 2:\n",
    "                i+=1\n",
    "            else:\n",
    "                break\n",
    "        if all([dic_rescore[key + i + ii] == 2 for ii in range(1,11)]):\n",
    "            for ii in range(1,i):\n",
    "                dic_rescore[key+i-ii] = 2\n",
    "                \n",
    "for key in dic_rescore.keys():# rule (e)\n",
    "    if (key > 19 and key < len(dic_rescore)-31 and all([dic_rescore[key-i] == 2 for i in range(1,21)])and dic_rescore[key + 1] < 2):\n",
    "        i=2\n",
    "        while(i<=10):\n",
    "            if dic_rescore[key+i] < 2:\n",
    "                i+=1\n",
    "            else:\n",
    "                break\n",
    "        if all([dic_rescore[key + i + ii] == 2 for ii in range(1,21)]):\n",
    "            for ii in range(1,i):\n",
    "                dic_rescore[key+i-ii] = 2\n",
    "# plt.subplot(211)\n",
    "plt.title(\"sleep score by ZCM\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_score.values())\n",
    "plt.show()\n",
    "                \n",
    "                \n",
    "plt.title(\"sleep rescore-1 after 5 gole rules\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_rescore.values())\n",
    "plt.show()\n",
    "\n",
    "# print(list(dic_rescore.values()).count(1))\n",
    "# print(dic_rescore.values())\n",
    "# key = 9\n",
    "# if ( key < len(dic_rescore)-60 and all([dic_rescore[key + i] < 2 for i in range(1,61)])):\n",
    "#     print(\"yes\")\n",
    "\n",
    "#判断是否进入睡眠程序:检测到连续30/60分钟睡眠才进入睡眠监测程序——待模块化！！！\n",
    "\n",
    "monitor = 0\n",
    "\n",
    "for key in dic_rescore.keys():\n",
    "    if ( key < len(dic_rescore)-60 and all([dic_rescore[key + i] < 2 for i in range(1,61)])):\n",
    "        for tt in range(1,key):\n",
    "            dic_rescore[key - tt] == 2 \n",
    "        print(\"From\", m2t(m_start+key), \"enter sleep monitoring program...\")\n",
    "        monitor = 1\n",
    "        break\n",
    "if(monitor == 0):\n",
    "    dic_rescore = {t: 2 for t in range(t)}\n",
    "\n",
    "# 深睡浅睡规则待完善 ！！！\n",
    "\n",
    "\n",
    "# plt.subplot(212)\n",
    "plt.title(\"sleep rescore-2 after sleep monitoring program\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_rescore.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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THoxr6/+Wh3H/QhSVOMZ+KXBP+PkhYHmbOjcCR7r7wcAi4Ng467n7enefdPfJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1cbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to fall sleep: 04:55\n",
      "Time to get up: 06:54 ,and out of sleep monitoring program.\n",
      "Time of sleep: 1.98 hours\n",
      "Time of deep sleep: 0.00 hours\n",
      "Time of light sleep: 1.98 hours\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1829c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplot(212)\n",
    "plt.title(\"sleep rescore-2 after sleep monitoring program\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_rescore.values())\n",
    "plt.show()\n",
    "\n",
    "# 睡眠指标统计：入睡时间time_fall， 起床时间time_get， 睡眠总时长t_sleep, 深睡眠时长t_deep， 浅睡眠时长t_light，睡眠过程中清醒时长t_wake\n",
    "\n",
    "\n",
    "#如果检测点前10分钟清醒,检测点后10分钟睡眠，这判定为入睡时间点\n",
    "for key in dic_rescore.keys():\n",
    "    if dic_rescore[key]<2:\n",
    "        if (key > 9 and key < len(dic_rescore)-10 and all([dic_rescore[key-i] == 2 for i in range(1,11)]) and all([dic_rescore[key+i] < 2 for i in range(1,11)])):\n",
    "            time_fall = m2t(key+m_start)\n",
    "            print(\"Time to fall sleep:\", time_fall)\n",
    "            break\n",
    "        if (key < 9 or key > len(dic_rescore)-10):\n",
    "            time_fall = m2t(key+m_start)\n",
    "            print(\"Time to fall sleep:\", time_fall)\n",
    "            break\n",
    "# 相反的，如果检测点前10分钟睡眠，检测点后10分钟清醒，则判定为起床时间点\n",
    "for key in dic_rescore.keys():\n",
    "    if (key > 9 and key < len(dic_rescore)-10 and dic_rescore[key] == 2 and all([dic_rescore[key-i] < 2 for i in range(1,11)]) and all([dic_rescore[key+i] == 2 for i in range(1,11)])):\n",
    "        time_get = m2t(key+m_start)\n",
    "        print(\"Time to get up:\", time_get, \",and out of sleep monitoring program.\")\n",
    "        #退出睡眠监测程序，并重新检测入睡时间点，即起床后——未进入睡眠程序前的点赋值为2，此模块待完善！！！\n",
    "        for kk in range(key, t):\n",
    "            dic_rescore[kk] = 2\n",
    "#           print(dic_rescore[key])\n",
    "        break\n",
    "\n",
    "# 睡眠过程中的清醒时长，如果检测点前10分钟睡眠，清醒时间段后10分钟睡眠，则判定为睡眠过程中清醒 \n",
    "for key in dic_rescore.keys():\n",
    "    if (key > 9 and key < len(dic_rescore)-10 and dic_rescore[key] == 2 and all([dic_rescore[key-i] < 2 for i in range(1,11)])):\n",
    "        wake_in_sleep = 0\n",
    "        while (key+wake_in_sleep < len(dic_rescore)-10):\n",
    "            if (dic_rescore[key+wake_in_sleep] == 2):\n",
    "                wake_in_sleep+=1\n",
    "            else:\n",
    "                break\n",
    "        if all([dic_rescore[key+wake_in_sleep+i] < 2 for i in range(1,10)]):\n",
    "            print(\"Wake time during sleep:\", m2t(key+wake_in_sleep+m_start))\n",
    "            print(\"The length of wake time during sleep:\", wake_in_sleep,\"minutes\")\n",
    "#重定义深睡：超过20min的深度睡眠才被定义为深睡，否则重定义为浅睡\n",
    "key = 0\n",
    "count_deep = 0\n",
    "while(key < len(dic_rescore) - 20):\n",
    "    \n",
    "    if(dic_rescore[key] == 0 ):\n",
    "        key += 1\n",
    "        count_deep += 1\n",
    "    else:\n",
    "        if(count_deep > 0 and count_deep < 20):\n",
    "            for kk in range(key-count_deep,key):\n",
    "                dic_rescore[kk] = 1\n",
    "        count_deep = 0\n",
    "        key += 1\n",
    "\n",
    "\n",
    "t_deep = list(dic_rescore.values()).count(0)/60\n",
    "t_light = list(dic_rescore.values()).count(1)/60\n",
    "t_wake = list(dic_rescore.values()).count(2)/60\n",
    "t_sleep = t_deep + t_light\n",
    "\n",
    "\n",
    "print(\"Time of sleep:\", '%.2f' % t_sleep, \"hours\")\n",
    "print(\"Time of deep sleep:\", '%.2f' % t_deep,\"hours\")\n",
    "print(\"Time of light sleep:\", '%.2f' % t_light,\"hours\")\n",
    "# print(\"Time of wake:\", '%.2f' % t_wake,\"hours\")\n",
    "\n",
    "plt.title(\"sleep rescore-3 after get-up monitoring program\")\n",
    "plt.xticks([tt for tt in range(m_start,t+m_start,60)],\n",
    "  [m2t(tt) for tt in range(m_start,t+m_start,60)])\n",
    "plt.plot(axis_m, dic_rescore.values())\n",
    "plt.show()"
   ]
  },
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   },
   "outputs": [],
   "source": []
  }
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